\(I_D\) , \(M_{rv}\) , \(I_R\)  and \(M_{bv}\)  are concatenated together as the input, as shown in Fig. REF . HDRNet then uses these data to recover the appearance content of the occluded parts and inpaints the distracting hand to avoid ambiguity. For model architecture choice, we follow [1]}, [2]} to adopt the network of Liu et al.  [3]} and further improve it by adding a few transformer blocks [4]}. The transformer block enhances image feature interactions, enlarges the receptive fields, and focuses more on important image regions. Finally, the HDRNet outputs a recovered image \(I_o\) . We follow [1]} to employ an image discriminator [6]} \(D\)  to enhance the image recovery quality through adversarial training.
The loss function of HDRNet is as follows:
\(\begin{aligned}\mathcal {L}_{HDR}= & \lambda _1 (\mathbb {E}_{I_o}[\mathrm {log}(1-D(I_o))] + \mathbb {E}_{I_o^*}[\mathrm {log}(D(I_o^*))]) + \\& \lambda _2 \mathcal {L}_{\ell 1}(I_o, I_o^*) + \lambda _3 \mathcal {L}_{prec}(I_o, I_o^*) + \lambda _4 \mathcal {L}_{style}(I_o, I_o^*),\end{aligned}\) 
